Robontics 2025 - Ontology-Guided Multi-Modal Perception for Trusted and Explainable Robotic Action
Year: 2025
Type: conference-paper
Abstract: This repository contains the implementation resources, ontological models, and experimental data accompanying the paper “Ontology-Guided Multi-Modal Perception for Trusted and Explainable Robotic Action. The dataset is based on the GOOSE multi-modal navigation benchmark, which includes synchronized RGB, depth, and telemetry sequences recorded for autonomous navigation and driving research. The provided files include preprocessed perception masks, ontology assertions (RDF/OWL-Lite), SHACL validation rules, and reasoning logs used in the experiments. The code implements the complete neuro-symbolic reasoning pipeline: perception (SegFormer/DeepLabV3), ontology mapping, SHACL-SPARQL constraint validation, and explanation generation modules.All experiments were conducted in a reproducible Colab environment with open-source dependencies (PyTorch, Transformers, rdflib, pySHACL). The resources are released under a permissive license for academic and research use, ensuring transparency, traceability, and reproducibility.
Authors: Rehman, Tayyab, De Gasperis, Giovanni, Facchini, Sante Dino
Institution: University of L'Aquila
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7102402915
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